Sensors used in various measurement systems convert measurands (physical parameters) into electrical signals. Signal processing circuits typically provide further amplification of such raw signals coming from the sensor, as well as linearization and temperature compensation. In this context, linearization is the ability of the device to closely approximate a linear relationship between the actual physical parameter being sensed (the measurand) and the device's output representing that physical parameter. Temperature compensation is the ability of the device to produce output signals which are relatively immune to variations of ambient temperature.
The accuracy and efficiency of linearization approximation algorithms depend on the type and extent of sensor non-linearity. In some cases linearization of an essentially non-linear sensor requires high order polynomial approximations, or approximations by other functions, using a large number of approximation coefficients. For accurate temperature-compensated measurements, these coefficients determined with high precision must also be temperature-dependent in order to correct temperature related aberrations of the sensor's non-linear response. As a result, the requirements for precision and computational resources used in post-calibration signal processing may become a burden.
Therefore, there is a need to develop new techniques to linearize and perform temperature compensation of various types of sensors.